I am currently trying to run experiments in parallel using MATLAB 2011b that are very time-consuming. I am wondering if someone could help me 'translate' the following block of generic (non-working) parfor code into something that will work in the spmd code.
amountOfOptions = 8;
startStockPrice = 60 + 40 * rand(1,amountOfOptions);
strike = 70 + 20 * rand(1,amountOfOptions);
v = 0.35 + 0.3 * rand(1,amountOfOptions);
IV = 0.25 + 0.1 * rand(1,amountOfOptions);
sigma = 0.15 + 0.65 * rand(1,amountOfOptions);
riskFreeRate = 0.05 + 0.1 * rand(1,amountOfOptions);
tn = fix(1 + 3 * rand(1,amountOfOptions));
tic;
for g=1:amountOfOptions
for i=1:10
N = i*5;
Cti = zeros(1,N);
Sti = zeros(1,N);
B = zeros(1,N);
d1_ti = zeros(1,N);
delta_t = zeros(1,N);
ctn = 0;
cmtn = 0;
result = 0;
t = (1:N)/N;
dt = 1/N;
c_mt0 = 0;
for j=1:10
B = sigma(g)*randn(1,N);
part1 = startStockPrice(g)*normcdf((log(startStockPrice(g)/strike(g))+(riskFreeRate(g)+(0.5*(IV(g))^2))*(tn))/(v(g)*sqrt(tn)),0,sigma(g));
part2 = exp(-riskFreeRate(g)*tn)*strike(g)*normcdf((log(startStockPrice(g)/strike(g))+(riskFreeRate(g)-(0.5*(IV(g))^2))*(tn))/(IV(g)*sqrt(tn)));
c_mt0 = part1 - part2;
Sti(1) = startStockPrice(g);
for j = 2:N-1
Sti(j)=Sti(j-1)*exp( (riskFreeRate(g)-dt*0.5*sigma(g)^2) * t(j)*dt + sigma(g)*B(j));
end
Sti(N) = Sti(N-1)*exp( (riskFreeRate(g)-dt*0.5*sigma(g)^2) * t(N)*dt + sigma(g)*B(N));
parfor i = 1:N-1
d1ti(i) = (log(Sti(i)/strike(g)) + (riskFreeRate(g) + v(g).^2/2) * (tn - t(i))) / (v(g) * sqrt(tn - t(i)));
end
parfor i = 1:N-1
Cti(i) = Sti(i).*normcdf((d1ti(i)),0,sigma(g)) - exp(-riskFreeRate(g).*(tn(g) - t(i))).*strike(g).*normcdf(((d1ti(i) - v(g)*sqrt(tn(g) - t(i)))) , 0 ,sigma(g));
end
if((Sti(N) - strike(g)) > 0)
ctn = Sti(N) - strike(g);
else
ctn = 0;
end
parfor i = 1:N-1
delta_t(i) = normcdf((d1ti(i)),0,sigma(g));
end
cmtn = ctn - c_mt0*exp(riskFreeRate(g)*tn(g));
result= cmtn + result;
end
result= result/10;
end
end
time = toc;
I've always used parfor over spmd because it's more logical for me. Since parfor requires that each iteration within the loop be independent of all other iterations. It's as easy as encapsulating it using the following method.
% Initial Variables
amountOfOptions = 8;
startStockPrice = 60 + 40 * rand(1,amountOfOptions);
strike = 70 + 20 * rand(1,amountOfOptions);
v = 0.35 + 0.3 * rand(1,amountOfOptions);
IV = 0.25 + 0.1 * rand(1,amountOfOptions);
sigma = 0.15 + 0.65 * rand(1,amountOfOptions);
riskFreeRate = 0.05 + 0.1 * rand(1,amountOfOptions);
tn = fix(1 + 3 * rand(1,amountOfOptions));
% Open Parpool
try
parpool;
catch
end
% Use parfor
parfor i = 1:amountOfOptions
[startStockPrice(i),strike(i),v(i),IV(i),sigma(i),riskFreeRate(i),tn(i)] = fun( startStockPrice(i),strike(i),v(i),IV(i),sigma(i),riskFreeRate(i),tn(i) );
end
Then you can create the encapsulating function fun that will accept all the parameters and process/reoutput them. It will have the following definition/header:
function [startStockPrice,strike,v,IV,sigma,riskFreeRate,tn] = fun( startStockPrice,strike,v,IV,sigma,riskFreeRate,tn );